Optimization of drilling process parameters of AA7150/MoO3 composites using Taguchi method

Vasudevan, Anupama; Kolappan, S.; Subramani, K.

doi:10.1016/j.matpr.2021.04.214

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…In addition, no discernible difference in tool performance was observed as a result of coating wear. Hence, even under the unpredictable coating wear, there was no substantial impact on MRR in our investigation [24]. To increase the Material Removal Rate, the influence of cooling and lubricating fluids while machining AA6063 Aluminium Alloy could be helpful which could be included in the future scope.…”

Section: Discussionmentioning

confidence: 89%

Machining of aluminium 6063 alloy using high strength steel and titanium nitride novel coated drill tool using CNC and comparing the material removal rate

Reddy

Vinodh

2023

J. Phys.: Conf. Ser.

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This study compares two tools, HSS M42 and TiN Coated twist drill bits, in order to determine which tool has the better material removal rate (MRR). Drill bits were of high speed steel (HSS M42) tool and use of Novel coating of Titanium Nitride on HSS tool in this study. The machining is done on a experimental specimen made of aluminium alloy AA6063. The material removal rate of both tools is measured in CNC drilling on two sets of 20 specimens each. Spindle speed, depth of cut, and feed were all controlled variables. G power 80% used for calculating sample size. SPSS software was used to record and analyse the results for both tools. The average MRR recorded for HSS M42 tool was 14.9803 mm3/s with a Standard deviation value 1.713221. This study has a significance value of 0.037 (p < 0.05) with a g power of 80%. The titanium nitride coated tool had an average MRR of 20.06995 mm3/s and 1.179870 standard deviation. The results were achieved at 0.037 significance, that is lower than 0.05. The titanium nitride coated drill bit has comparatively better MRR than the high speed steel drill bit in no-lubricant and no-coolant drilling circumstances, as per this study.

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Section: Discussionmentioning

confidence: 89%

Machining of aluminium 6063 alloy using high strength steel and titanium nitride novel coated drill tool using CNC and comparing the material removal rate

Reddy

Vinodh

2023

J. Phys.: Conf. Ser.

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“…The results of ANOVA showed that the Ra was greatly influenced by feed with contribution of 24.27%. The results revealed that tool diameter was most important for achieving better surface finish [17]. Umesh gowda et al…”

Section: Introductionmentioning

confidence: 93%

Optimization of machining parameters in drilling of aluminium matrix composite (Al7050-12wt.% ZrO2) using Taguchi technique

Kumar¹,

Alagarsamy

Hossain³

et al. 2023

Preprint

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This study focused on optimizing the drilling variables for minimizing the surface roughness (Ra) of Al7050 alloy based composite using Taguchi technique. The stir casting route was adopted to synthesis the Al7050 matrix composite with addition of 12 wt. % ZrO2 particles as reinforcement. The different process parameters such as spindle speed (v), feed (f), and depth of cut (d) were chosen as the input parameters, while the Ra was considered as the output response. Based on the parameters selection, the drilling process was carried out on Taguchi L16 orthogonal design. The SN (signal-to-noise) ratio and ANOVA (analysis of variance) were applied to yield the optimal setting of parameters and their impact contribution on Ra for the drilled composite. The SN ratio results identified that the minimum value of Ra was achieved at 1600 rpm of spindle speed, 0.12 mm/rev of feed and 1.0 mm of depth of cut. ANOVA result observed that the most noteworthy parameter for Ra was feed (62.24%), followed by spindle speed (26.24%). The contour graphs revealed that the Ra value drastically reduced when an increase in depth of cut (d) and decrease in feed (f). The regression model has been formulated to establishing the relationship of drilling parameters on Ra.

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“…Notably, these arrays possess reconfigurable properties, allowing for dynamic modifications in beam formations. This adaptability in altering beams designates these active antenna arrays as reconfigurable antenna arrays, providing flexibility and adaptiveness across diverse communication scenarios [43,44].…”

Section: Phased Antenna Arraysmentioning

confidence: 99%

Enhancing Beamforming Efficiency: Utilizing Taguchi Optimization and Neural Network Acceleration

Kheder,

Ghayoula,

Smida

et al. 2024

Preprint

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The article presents an innovative method for synthesizing radiation patterns efficiently by 1 combining the Taguchi method and neural networks, while validating the results on a 10-element 2 antenna array. The Taguchi method aims to minimize product and process variability, while neural 3 networks are used to model the relationship between antenna design parameters and radiation 4 pattern characteristics. This approach utilizes Taguchi parameters as inputs for the neural network, 5 which is then trained on a dataset generated by the Taguchi method. After training, the network is 6 validated using a real 10-element antenna array. Analytical results demonstrate that this method 7 enables efficient synthesis of radiation patterns with a significant reduction in computation time 8 compared to traditional approaches. Furthermore, validation on the antenna array confirms the 9 accuracy and robustness of the approach, showing a high correlation between predicted performances 10 by the neural network model and actual measurements on the antenna array.In summary, our article 11 highlights that the combined use of the Taguchi method and neural networks, with validation on a 12 real antenna array, offers a promising approach for efficient synthesis of antenna radiation patterns. 13 This approach combines speed, accuracy, and reliability in antenna system design.

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Optimization of drilling process parameters of AA7150/MoO3 composites using Taguchi method

Cited by 4 publications

References 16 publications

Machining of aluminium 6063 alloy using high strength steel and titanium nitride novel coated drill tool using CNC and comparing the material removal rate

Machining of aluminium 6063 alloy using high strength steel and titanium nitride novel coated drill tool using CNC and comparing the material removal rate

Optimization of machining parameters in drilling of aluminium matrix composite (Al7050-12wt.% ZrO2) using Taguchi technique

Enhancing Beamforming Efficiency: Utilizing Taguchi Optimization and Neural Network Acceleration

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